Method and apparatus for automatic object identification

• US 20110103648A1
• Filed: 09/24/2010
• Published: 05/05/2011
• Est. Priority Date: 07/18/2003
• Status: Active Grant

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Wiedemann, Melissa, Skoblick, Richard, Phurrough, Larry, Gratsch, Colleen Flynn, Lee, Harry C.
• Granted Patent: US 8,081,799 B2
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06V 20/13 Satellite images
• CPC Class Codes: G06V 30/2504 Coarse or fine approaches, e.g. resolution of ambiguities or multiscale approaches

Abstract

A method and system for processing image data to identify objects in an image. A gradient vector image is generated from the image, the gradient vector image identifying a gradient magnitude value and a gradient direction for each pixel of the image. Lines are identified in the gradient vector image. It is determined whether the identified lines are perpendicular, whether more than a predetermined number of pixels on each of the lines identified as perpendicular have a gradient magnitude greater than a predetermined threshold, and whether the individual lines which are identified as perpendicular are within a predetermined distance of each other. A portion of the image is identified as an object if the identified lines are perpendicular, more than the predetermined number of pixels on each of the lines have a gradient magnitude greater than the predetermined threshold, and are within a predetermined distance of each other.

Claims

1. A method for identifying objects in an image comprising:

generating a gradient vector image from the image, the gradient vector image identifying a gradient magnitude value and a gradient direction for each pixel of the image;

identifying lines in the gradient vector image;

determining whether the identified lines are perpendicular;

determining whether more than a predetermined number of pixels on each of the lines identified as perpendicular have a gradient magnitude greater than a predetermined threshold;

determining whether the individual lines which are identified as perpendicular are within a predetermined distance of each other;

identifying a portion of the image as an object if the identified lines are perpendicular, more than the predetermined number of pixels on each of the lines have a gradient magnitude greater than the predetermined threshold, and are within a predetermined distance of each other.

2. The method of claim 1, wherein the step of identifying a portion of the image as an object further comprises:

calculating a set of features for each portion of the image identified as an object; and

classifying the portion of the image identified as an object using said set of features.

3. The method of claim 2, wherein said set of features comprises:

area, average intensity, standard deviation, global intensity, gray contrast, edge magnitude, average mean edge magnitude, morphological filter statistics, structure confidence, and texture energy filters.

4. A computer-readable recording medium having a computer program recorded thereon that causes a computer to identify objects in an image, the program causing the computer to perform operations comprising:

generating a gradient vector image from the image, the gradient vector image identifying a gradient magnitude value and a gradient direction for each pixel of the image;

identifying lines in the gradient vector image;

determining whether the identified lines are perpendicular;

determining whether more than a predetermined number of pixels on each of the lines identified as perpendicular have a gradient magnitude greater than a predetermined threshold;

determining whether the individual lines which are identified as perpendicular are within a predetermined distance of each other;

identifying a portion of the image as an object if the identified lines are perpendicular, more than the predetermined number of pixels on each of the lines have a gradient magnitude greater than the predetermined threshold, and are within a predetermined distance of each other.

5. The computer readable medium of claim 4, wherein the operation of identifying a portion of the image as an object further comprises:

calculating a set of features for each portion of the image identified as an object; and

classifying the portion of the image identified as an object using said set of features.

6. The method of claim 5, wherein said set of features comprises:

area, average intensity, standard deviation, global intensity, gray contrast, edge magnitude, average mean edge magnitude, morphological filter statistics, structure confidence, and texture energy filters.